In addition to faker, there are several Python libraries and tools that can generate reliable synthetic data, depending on the type (tabular, time-series, image, text, etc.) and the level of realism or constraints you need. Here’s a breakdown by use case:
1. General Tabular Data
- SDV (Synthetic Data Vault)
- State-of-the-art for realistic synthetic tabular data.
- Uses statistical modeling or deep learning (GANs, CTGAN, TVAE).
- Ideal for privacy-preserving data generation.
- scikit-learn’s make_classification, make_regression, make_blobs
- Useful for ML model prototyping.
- Generates labeled synthetic data for classification/regression problems.
- [Synthpop (via RPy2 for Python)]
- If you want to use R’s synthpop from Python.
- Great for statistical privacy-preserving synthetic data.
2. Time-Series Data
- Timesynth
- For generating synthetic time series data.
- Supports autoregressive signals, harmonic signals, and noise.
- Sktime
- Has utilities for time series generation, often used in research.
3. Image / Vision Data
- Albumentations / ImgAug
- Not for raw image generation, but great for data augmentation.
- StyleGAN2 / DALL·E (for advanced users)
- Used to generate high-quality synthetic images.
4. Text / NLP Data
- TextAttack
- For generating paraphrases, adversarial samples.
- Useful for NLP model testing.
- Transformers (HuggingFace)
- Fine-tuned models can generate domain-specific synthetic text.
5. Domain-Specific Libraries
- Pydbgen
- Like Faker, but tailored for fake databases.
- DataSynthesizer
- Generates synthetic data with differential privacy guarantees.
- Mimesis
- Similar to Faker, supports multiple locales and categories.
Would you like suggestions based on a specific type of data or project goal (e.g., simulating customers, financial records, health data)?